import os from typing import Union from transformers import PretrainedConfig, PhiConfig from transformers.utils import logging logger = logging.get_logger(__name__) class LlavaPhiVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). mm_vision_select_feature (`str`, *optional*, defaults to `"patch"`): The feature to select from the vision encoder output. Can be one of `"patch"` or `"cls_patch"`. mm_vision_select_layer (`int`, *optional*, defaults to `-2`): The layer to select from the vision encoder output. Example: ```python >>> from transformers import CLIPVisionConfig, CLIPVisionModel >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPVisionConfig() >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "llava_phi_clip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, projection_dim=512, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=32, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, mm_vision_select_feature="patch", mm_vision_select_layer=-2, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.image_size = image_size self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.mm_vision_select_feature = mm_vision_select_feature self.mm_vision_select_layer = mm_vision_select_layer @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from CLIPConfig if config_dict.get("model_type") == "llava_phi-phi": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class ProjectorConfig(PretrainedConfig): model_type = "llava_phi_projector" def __init__( self, mm_projector_type="linear", mm_hidden_size=768, hidden_size=2560, **kwargs ): self.mm_projector_type = mm_projector_type self.mm_hidden_size = mm_hidden_size self.hidden_size = hidden_size super().__init__(**kwargs) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from CLIPConfig if config_dict.get("model_type") == "llava_phi-phi": config_dict = config_dict["projector_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) DEFAULT_VISUAL_CONFIG = { "vision_tower": LlavaPhiVisionConfig().to_dict(), "mm_projector": ProjectorConfig().to_dict() } class LlavaPhiConfig(PhiConfig): model_type = "llava_phi" def __init__(self, vision_config=None, **kwargs): if vision_config is None: self.vision_config = DEFAULT_VISUAL_CONFIG else: self.vision_config = vision_config super().__init__(**kwargs) if __name__ == "__main__": print(LlavaPhiVisionConfig())